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Found 13 papers, 5 papers with code
Training Large ASR Encoders with Differential Privacy
In this paper, we apply differentially private (DP) pre-training to a SOTA Conformer-based encoder, and study its performance on a downstream ASR task assuming the fine-tuning data is public.
Using Unsupervised and Supervised Learning and Digital Twin for Deep Convective Ice Storm Classification
Critical to the intelligent targeting is accurate identification of storm/cloud types from eight bands of radiance collected by the radiometer.
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between.
Enabling Astronaut Self-Scheduling using a Robust Advanced Modelling and Scheduling system: an assessment during a Mars analogue mission
Human long duration exploration missions (LDEMs) raise a number of technological challenges.
Temporal Multimodal Multivariate Learning
We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcome variable from one time stage to another.
Detecting Unintended Memorization in Language-Model-Fused ASR
End-to-end (E2E) models are often being accompanied by language models (LMs) via shallow fusion for boosting their overall quality as well as recognition of rare words.
Toward Training at ImageNet Scale with Differential Privacy
Despite a rich literature on how to train ML models with differential privacy, it remains extremely challenging to train real-life, large neural networks with both reasonable accuracy and privacy.
Membership Inference Attacks From First Principles
A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training dataset.
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
We study the problem of differentially private (DP) matrix completion under user-level privacy.
Using Explainable Scheduling for the Mars 2020 Rover Mission
In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior.
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data.
Making the Shoe Fit: Architectures, Initializations, and Tuning for Learning with Privacy
Because learning sometimes involves sensitive data, standard machine-learning algorithms have been extended to offer strong privacy guarantees for training data.
A General Approach to Adding Differential Privacy to Iterative Training Procedures
In this work we address the practical challenges of training machine learning models on privacy-sensitive datasets by introducing a modular approach that minimizes changes to training algorithms, provides a variety of configuration strategies for the privacy mechanism, and then isolates and simplifies the critical logic that computes the final privacy guarantees.
